Offline Time-Independent Multi-Agent Path Planning
Authors: Keisuke Okumura, François Bonnet, Yasumasa Tamura, Xavier Défago
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present algorithms to solve OTIMAPP and demonstrate the utility of OTIMAPP via robotic applications... Section 6 shows that either PP or DBS can compute large OTIMAPP instances to some extent. Furthermore, we show that solutions keep robots moves efficient in an adverse environment for timing assumptions compared to existing approaches with runtime supports... Moreover, we demonstrate that solutions are executable with physical robots... This section empirically demonstrates that OTIMAPP solutions are computable to some extent (Sec. 6.1) and they are useful in adverse environments about timings (Sec. 6.2) through the simulation experiments. We also present OTIMAPP execution with robots (Sec. 6.3). |
| Researcher Affiliation | Academia | Keisuke Okmura , Franc ois Bonnet , Yasumasa Tamura and Xavier D efago Tokyo Institute of Technology {okumura.k, bonnet.f, tamura.y, defago.x}@coord.c.titech.ac.jp |
| Pseudocode | Yes | Algorithm 1 PP: Prioritized Planning and Algorithm 2 DBS: Deadlock-based Search. |
| Open Source Code | Yes | The appendix, code, and movie are available on https://kei18.github.io/otimapp. |
| Open Datasets | No | The paper describes generating instances on 'four-connected undirected grids picked up from [Stern et al., 2019]' and 'random graphs'. It states 'All instances were generated by setting a start si and a goal gi randomly'. However, it does not provide concrete access (URL, DOI, or specific citation to a dataset repository) for these generated instances or the random graphs used in their experiments, nor does it refer to a universally recognized public dataset by name that guarantees access. |
| Dataset Splits | No | The paper mentions running experiments on '25 identical instances' and '10 instances that OTIMAPP solutions were found by PP+' with '50 trials while changing the random seed'. However, it does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits) for the data used in the experiments. |
| Hardware Specification | Yes | The simulator was coded in C++ and the experiments were run on a desktop PC with Intel Core i9 2.8 GHz CPU and 64 GB RAM. |
| Software Dependencies | No | The paper mentions 'The simulator was coded in C++' but does not provide specific version numbers for the C++ compiler, any libraries, or other software dependencies used. |
| Experiment Setup | No | The paper describes general experimental settings such as 'timeout of 5 min' and how instances were generated (random start/goal pairs), and mentions the use of specific heuristics for the DBS solver ('descending order of the number of deadlocks with two agents'). However, it does not provide specific hyperparameter values or detailed system-level training settings like learning rates, batch sizes, or optimizer configurations that would typically be found in an 'experimental setup' section. |